Thanks for another citation by Pavel Andrlik, at the Master Thesis on OCR using deep learning.
University of West Bohemia
Faculty of Applied Sciences
Department of Cybernetics
(Czech Republic)
The author has been highlight my paper over the classification method choice on using Support Vector machine...[]
Abstract of the Thesis
This diploma thesis deals with the problem of optical character recognition (OCR) using neural networks. I am focusing on improving text detection and OCR by fine-tuning an E2E-MLT scene text detector by training it on synthetic data which emulates real data. The model was fine-tuned on several datasets with synthetically generated data and real data, then the models were tested on one synthetic and two real datasets, one with the majority of the wild text, the second with the majority of TV news imprinted text. On the dataset with majority of TV news imprinted texts the fine-tuned models achieved improvement by decreasing character error rate from 52% to 31.6% word error rate and from 56.5% to 22%. It was also experimentally discovered that training models on synthetic data simulating real TV news images deteriorate detection and reading model capability on wild text data.
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What I am interesting is at the motivation side!
My quick reflection on the motivation side!
The use case could also apply on some written paper for data collection such as on artist idea, random articles etc. we have a lot of handwriting or piece of writing printed that should also consider as collection on our language.
